This invention is directed generally to predictive maintenance and more particularly to detection of motor bearing faults.
Induction machines are called the workhorses of industry due to their widespread use in manufacturing. The heavy reliance of industry on these machines in critical applications makes catastrophic motor failures very expensive. Vibration, thermal, and acoustic analyses are some of the commonly used methods, for predictive maintenance, to monitor the health of the machine to prevent motor failures from causing expensive shut-downs. Preventive maintenance of induction motors plays an important role in avoiding expensive shut-downs due to motor failures. Vibration and thermal monitoring require additional sensors or transducers to be fitted on the machines. While some large motors may already come with vibration and thermal transducers, it is not economically or physically feasible to provide the same for smaller machines.
Continuous monitoring with expensive analyzers may not be feasible, so that motors are checked periodically by technicians moving portable equipment from machine to machine on a maintenance schedule. The migration of multi-function circuit monitors from the principal feeders toward individual loads has propelled a study of the relationship of bearing vibration to the stator current. Current monitoring provides a non-intrusive approach to continuously monitor motor reliability with minimal additional cost.
Motor current signature analysis (MCSA) provides a non-intrusive way to obtain information about bearing health using already available line current. MCSA gets bearing information by relating the current spectral frequencies to characteristic vibration frequencies. Vibration signals from a defective bearing often consist of a superposition of normal bearing noise and the impulse response due to the defects. The characteristic vibration frequencies are calculated using rotor speed and the bearing geometry. MCSA investigates steady state data and utilizes the Fourier Analysis as the primary frequency domain method in determining bearing related spectral components.
Presently, motor current signature analysis techniques cannot detect bearing faults until the bearing fault reaches advanced stages. This increases the risk of a catastrophic failure.
Presently available motor current signature analysis techniques concerned with bearing fault diagnosis use the motor current data collected under steady state conditions. In steady state, the frequency components of motor current caused by bearing faults are very small compared to the rest of the current spectrum. Due to the large difference between current spectrum bearing harmonics and the rest of the current spectrum, it is much more difficult to detect bearing harmonics in the current spectrum.
In this invention, the starting current transient of an induction motor is analyzed via discrete wavelet transform to detect bearing faults. The frequency sub-bands for bearing pre-fault and post-fault conditions are compared to identify the effects of bearing/machine resonant frequencies as the motor starts. Using starting current transient analysis via discrete wavelet transform, the motor bearing faults are detected at an earlier stages so that bearing replacement can be scheduled and down time can be minimized.
Briefly, in accordance with the foregoing, a method for detecting motor bearing defects comprises obtaining motor current transient data during motor start-up, and analyzing the motor current transient data to detect changes in RMS levels due to bearing defect-induced resonance.
In accordance with another embodiment of the invention, an apparatus for detecting motor bearing defects comprises means for obtaining motor current transient data during motor start-up, and means for analyzing the motor current transient data to detect changes in RMS levels due to bearing defect-induced resonance.